Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations149214
Missing cells74
Missing cells (%)< 0.1%
Duplicate rows173
Duplicate rows (%)0.1%
Total size in memory21.6 MiB
Average record size in memory152.0 B

Variable types

Text2
Numeric15
Categorical2

Alerts

crawling_date has constant value "20241130" Constant
Dataset has 173 (0.1%) duplicate rowsDuplicates
Topic_3 is highly overall correlated with breadth and 1 other fieldsHigh correlation
avg_rating is highly overall correlated with num_of_ratings and 1 other fieldsHigh correlation
breadth is highly overall correlated with Topic_3High correlation
depth is highly overall correlated with Topic_3High correlation
num_of_enrolled is highly overall correlated with num_of_ratings and 2 other fieldsHigh correlation
num_of_ratings is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
num_of_reviews is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
num_of_top_instructor_courses is highly overall correlated with num_of_top_instructor_leanersHigh correlation
num_of_top_instructor_leaners is highly overall correlated with num_of_enrolled and 3 other fieldsHigh correlation
Rating is highly imbalanced (62.4%) Imbalance
helpfulness is highly skewed (γ1 = 44.44029739) Skewed
helpfulness has 143344 (96.1%) zeros Zeros

Reproduction

Analysis started2025-01-14 14:00:34.447494
Analysis finished2025-01-14 14:01:27.964629
Duration53.52 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:28.283376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length79
Median length56
Mean length21.447425
Min length2

Characters and Unicode

Total characters3200256
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowfoundations-of-cybersecurity
2nd rowfoundations-of-cybersecurity
3rd rowfoundations-of-cybersecurity
4th rowfoundations-of-cybersecurity
5th rowfoundations-of-cybersecurity
ValueCountFrequency (%)
foundations-user-experience-design 9972
 
6.7%
python 9965
 
6.7%
python-data 9964
 
6.7%
python-network-data 7107
 
4.8%
html 6620
 
4.4%
foundations-of-cybersecurity 5509
 
3.7%
html-css-javascript-for-web-developers 4128
 
2.8%
matlab 4017
 
2.7%
introduction-tensorflow 3800
 
2.5%
python-basics 3784
 
2.5%
Other values (195) 84348
56.5%
2025-01-14T23:01:29.016902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 295540
 
9.2%
e 272293
 
8.5%
t 269829
 
8.4%
o 252207
 
7.9%
n 240382
 
7.5%
a 213986
 
6.7%
r 204744
 
6.4%
i 200907
 
6.3%
s 179775
 
5.6%
d 128658
 
4.0%
Other values (20) 941935
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3200256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 295540
 
9.2%
e 272293
 
8.5%
t 269829
 
8.4%
o 252207
 
7.9%
n 240382
 
7.5%
a 213986
 
6.7%
r 204744
 
6.4%
i 200907
 
6.3%
s 179775
 
5.6%
d 128658
 
4.0%
Other values (20) 941935
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3200256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 295540
 
9.2%
e 272293
 
8.5%
t 269829
 
8.4%
o 252207
 
7.9%
n 240382
 
7.5%
a 213986
 
6.7%
r 204744
 
6.4%
i 200907
 
6.3%
s 179775
 
5.6%
d 128658
 
4.0%
Other values (20) 941935
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3200256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 295540
 
9.2%
e 272293
 
8.5%
t 269829
 
8.4%
o 252207
 
7.9%
n 240382
 
7.5%
a 213986
 
6.7%
r 204744
 
6.4%
i 200907
 
6.3%
s 179775
 
5.6%
d 128658
 
4.0%
Other values (20) 941935
29.4%

posted_date
Real number (ℝ)

Distinct3382
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20207572
Minimum20150813
Maximum20241201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:29.180947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum20150813
5-th percentile20170125
Q120200422
median20201211
Q320221115
95-th percentile20240613
Maximum20241201
Range90388
Interquartile range (IQR)20693

Descriptive statistics

Standard deviation21025.925
Coefficient of variation (CV)0.0010404974
Kurtosis-0.21310452
Mean20207572
Median Absolute Deviation (MAD)18906
Skewness-0.40892796
Sum3.0152526 × 1012
Variance4.4208953 × 108
MonotonicityNot monotonic
2025-01-14T23:01:29.330698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200531 293
 
0.2%
20200530 282
 
0.2%
20200601 282
 
0.2%
20200602 275
 
0.2%
20200525 273
 
0.2%
20200518 273
 
0.2%
20200511 273
 
0.2%
20200512 272
 
0.2%
20200713 269
 
0.2%
20200516 268
 
0.2%
Other values (3372) 146454
98.2%
ValueCountFrequency (%)
20150813 1
< 0.1%
20150815 2
< 0.1%
20150818 1
< 0.1%
20150819 1
< 0.1%
20150820 1
< 0.1%
20150826 1
< 0.1%
20150827 1
< 0.1%
20150828 1
< 0.1%
20150831 2
< 0.1%
20150903 1
< 0.1%
ValueCountFrequency (%)
20241201 1
 
< 0.1%
20241130 22
< 0.1%
20241129 31
< 0.1%
20241128 34
< 0.1%
20241127 27
< 0.1%
20241126 34
< 0.1%
20241125 45
< 0.1%
20241124 41
< 0.1%
20241123 42
< 0.1%
20241122 42
< 0.1%

crawling_date
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
20241130
149214 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1193712
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20241130
2nd row20241130
3rd row20241130
4th row20241130
5th row20241130

Common Values

ValueCountFrequency (%)
20241130 149214
100.0%

Length

2025-01-14T23:01:29.491554image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T23:01:29.626858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
20241130 149214
100.0%

Most occurring characters

ValueCountFrequency (%)
2 298428
25.0%
0 298428
25.0%
1 298428
25.0%
4 149214
12.5%
3 149214
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1193712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 298428
25.0%
0 298428
25.0%
1 298428
25.0%
4 149214
12.5%
3 149214
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1193712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 298428
25.0%
0 298428
25.0%
1 298428
25.0%
4 149214
12.5%
3 149214
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1193712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 298428
25.0%
0 298428
25.0%
1 298428
25.0%
4 149214
12.5%
3 149214
12.5%

Rating
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
5
123632 
4
18294 
3
 
4581
2
 
1467
1
 
1240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters149214
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 123632
82.9%
4 18294
 
12.3%
3 4581
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Length

2025-01-14T23:01:29.735381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T23:01:29.885065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 123632
82.9%
4 18294
 
12.3%
3 4581
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 123632
82.9%
4 18294
 
12.3%
3 4581
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 123632
82.9%
4 18294
 
12.3%
3 4581
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 123632
82.9%
4 18294
 
12.3%
3 4581
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 123632
82.9%
4 18294
 
12.3%
3 4581
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

avg_rating
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7422065
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:30.003874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.6
Q14.7
median4.8
Q34.8
95-th percentile4.9
Maximum4.9
Range1.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.10450413
Coefficient of variation (CV)0.022037026
Kurtosis4.1599676
Mean4.7422065
Median Absolute Deviation (MAD)0.1
Skewness-1.3379652
Sum707603.6
Variance0.010921112
MonotonicityNot monotonic
2025-01-14T23:01:30.123608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.8 70619
47.3%
4.7 40530
27.2%
4.6 18601
 
12.5%
4.9 13363
 
9.0%
4.5 3843
 
2.6%
4.4 1447
 
1.0%
4.3 624
 
0.4%
4.1 68
 
< 0.1%
4 44
 
< 0.1%
3.9 40
 
< 0.1%
Other values (2) 35
 
< 0.1%
ValueCountFrequency (%)
3.8 8
 
< 0.1%
3.9 40
 
< 0.1%
4 44
 
< 0.1%
4.1 68
 
< 0.1%
4.2 27
 
< 0.1%
4.3 624
 
0.4%
4.4 1447
 
1.0%
4.5 3843
 
2.6%
4.6 18601
12.5%
4.7 40530
27.2%
ValueCountFrequency (%)
4.9 13363
 
9.0%
4.8 70619
47.3%
4.7 40530
27.2%
4.6 18601
 
12.5%
4.5 3843
 
2.6%
4.4 1447
 
1.0%
4.3 624
 
0.4%
4.2 27
 
< 0.1%
4.1 68
 
< 0.1%
4 44
 
< 0.1%

num_of_ratings
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36088.212
Minimum13
Maximum229752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:30.269633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile1170
Q13661
median13222
Q327905
95-th percentile229752
Maximum229752
Range229739
Interquartile range (IQR)24244

Descriptive statistics

Standard deviation58192.169
Coefficient of variation (CV)1.612498
Kurtosis5.3220797
Mean36088.212
Median Absolute Deviation (MAD)10265
Skewness2.4606702
Sum5.3848664 × 109
Variance3.3863285 × 109
MonotonicityNot monotonic
2025-01-14T23:01:30.422048image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69498 9972
 
6.7%
229752 9965
 
6.7%
96077 9964
 
6.7%
44178 7107
 
4.8%
27564 6620
 
4.4%
27905 5509
 
3.7%
16672 4128
 
2.8%
17700 4017
 
2.7%
19521 3800
 
2.5%
17773 3784
 
2.5%
Other values (183) 84348
56.5%
ValueCountFrequency (%)
13 1
 
< 0.1%
19 6
 
< 0.1%
24 24
< 0.1%
28 8
 
< 0.1%
30 9
 
< 0.1%
39 18
< 0.1%
41 10
< 0.1%
42 13
< 0.1%
52 11
< 0.1%
55 11
< 0.1%
ValueCountFrequency (%)
229752 9965
6.7%
96077 9964
6.7%
69498 9972
6.7%
44178 7107
4.8%
27905 5509
3.7%
27564 6620
4.4%
21320 2694
 
1.8%
19521 3800
 
2.5%
17773 3784
 
2.5%
17700 4017
2.7%

helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15412763
Minimum0
Maximum239
Zeros143344
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:30.575571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum239
Range239
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0044574
Coefficient of variation (CV)13.005179
Kurtosis3245.5147
Mean0.15412763
Median Absolute Deviation (MAD)0
Skewness44.440297
Sum22998
Variance4.0178495
MonotonicityNot monotonic
2025-01-14T23:01:30.735331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143344
96.1%
1 3307
 
2.2%
2 809
 
0.5%
3 388
 
0.3%
4 282
 
0.2%
5 184
 
0.1%
6 141
 
0.1%
7 97
 
0.1%
8 89
 
0.1%
9 82
 
0.1%
Other values (68) 491
 
0.3%
ValueCountFrequency (%)
0 143344
96.1%
1 3307
 
2.2%
2 809
 
0.5%
3 388
 
0.3%
4 282
 
0.2%
5 184
 
0.1%
6 141
 
0.1%
7 97
 
0.1%
8 89
 
0.1%
9 82
 
0.1%
ValueCountFrequency (%)
239 1
< 0.1%
202 1
< 0.1%
144 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
111 1
< 0.1%
108 2
< 0.1%
99 2
< 0.1%
Distinct125712
Distinct (%)84.3%
Missing74
Missing (%)< 0.1%
Memory size1.1 MiB
2025-01-14T23:01:31.518303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6153
Median length2287
Mean length112.74496
Min length1

Characters and Unicode

Total characters16814783
Distinct characters146
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122708 ?
Unique (%)82.3%

Sample

1st rowThe course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field. It is very good for the beginners who are new to this field
2nd rowInformation was well organized, easy to learn, and study. with frequent note writing, and some breaks . You can learn a good brief summary of what's to come, and what to research more in the future.
3rd rowFor a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses. I'm eager to move on to the other courses now.
4th rowI think this is a great start for anyone who is starting from absolute zero. I think that since I've been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher!
5th rowSurprised by the quality of this course. repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable.
ValueCountFrequency (%)
the 128041
 
4.5%
course 97393
 
3.4%
and 94044
 
3.3%
to 93215
 
3.3%
a 66978
 
2.4%
i 64721
 
2.3%
of 51245
 
1.8%
this 48654
 
1.7%
is 45538
 
1.6%
for 43044
 
1.5%
Other values (56150) 2113949
74.3%
2025-01-14T23:01:32.415387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2709468
16.1%
e 1666031
 
9.9%
t 1115327
 
6.6%
o 1086231
 
6.5%
a 984851
 
5.9%
n 938466
 
5.6%
r 895382
 
5.3%
s 881657
 
5.2%
i 854188
 
5.1%
l 573469
 
3.4%
Other values (136) 5109713
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16814783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2709468
16.1%
e 1666031
 
9.9%
t 1115327
 
6.6%
o 1086231
 
6.5%
a 984851
 
5.9%
n 938466
 
5.6%
r 895382
 
5.3%
s 881657
 
5.2%
i 854188
 
5.1%
l 573469
 
3.4%
Other values (136) 5109713
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16814783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2709468
16.1%
e 1666031
 
9.9%
t 1115327
 
6.6%
o 1086231
 
6.5%
a 984851
 
5.9%
n 938466
 
5.6%
r 895382
 
5.3%
s 881657
 
5.2%
i 854188
 
5.1%
l 573469
 
3.4%
Other values (136) 5109713
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16814783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2709468
16.1%
e 1666031
 
9.9%
t 1115327
 
6.6%
o 1086231
 
6.5%
a 984851
 
5.9%
n 938466
 
5.6%
r 895382
 
5.3%
s 881657
 
5.2%
i 854188
 
5.1%
l 573469
 
3.4%
Other values (136) 5109713
30.4%

num_of_reviews
Real number (ℝ)

High correlation 

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3919.6104
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:32.584427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile208
Q1836
median2698
Q36623
95-th percentile10000
Maximum10000
Range9999
Interquartile range (IQR)5787

Descriptive statistics

Standard deviation3603.2184
Coefficient of variation (CV)0.91927974
Kurtosis-1.0433076
Mean3919.6104
Median Absolute Deviation (MAD)2112
Skewness0.70179697
Sum5.8486075 × 108
Variance12983183
MonotonicityNot monotonic
2025-01-14T23:01:32.748228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 29901
 
20.0%
7113 7107
 
4.8%
6623 6620
 
4.4%
5510 5509
 
3.7%
4131 4128
 
2.8%
4032 4017
 
2.7%
3809 3800
 
2.5%
3784 3784
 
2.5%
2854 2852
 
1.9%
2779 2775
 
1.9%
Other values (168) 78721
52.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
7 7
 
< 0.1%
8 40
< 0.1%
9 27
< 0.1%
10 30
< 0.1%
11 22
< 0.1%
12 12
 
< 0.1%
ValueCountFrequency (%)
10000 29901
20.0%
7113 7107
 
4.8%
6623 6620
 
4.4%
5510 5509
 
3.7%
4131 4128
 
2.8%
4032 4017
 
2.7%
3809 3800
 
2.5%
3784 3784
 
2.5%
2854 2852
 
1.9%
2779 2775
 
1.9%

num_of_enrolled
Real number (ℝ)

High correlation 

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675917.06
Minimum1507
Maximum3205753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:32.902935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1507
5-th percentile65412
Q1182631
median408135
Q3988223
95-th percentile3205753
Maximum3205753
Range3204246
Interquartile range (IQR)805592

Descriptive statistics

Standard deviation777385.76
Coefficient of variation (CV)1.1501201
Kurtosis4.6339143
Mean675917.06
Median Absolute Deviation (MAD)261381
Skewness2.2312796
Sum1.0085629 × 1011
Variance6.0432862 × 1011
MonotonicityNot monotonic
2025-01-14T23:01:33.067787image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361423 9972
 
6.7%
3205753 9965
 
6.7%
1076350 9964
 
6.7%
682235 7107
 
4.8%
574559 6620
 
4.4%
988223 5509
 
3.7%
1171384 4128
 
2.8%
497579 4017
 
2.7%
385916 3800
 
2.5%
459997 3784
 
2.5%
Other values (195) 84348
56.5%
ValueCountFrequency (%)
1507 8
< 0.1%
2165 11
< 0.1%
2233 6
< 0.1%
2240 13
< 0.1%
2429 7
< 0.1%
3736 8
< 0.1%
3802 1
 
< 0.1%
4033 14
< 0.1%
5198 12
< 0.1%
5932 10
< 0.1%
ValueCountFrequency (%)
3205753 9965
6.7%
1361423 9972
6.7%
1171384 4128
2.8%
1076350 9964
6.7%
988223 5509
3.7%
682235 7107
4.8%
582205 2714
 
1.8%
574559 6620
4.4%
540079 2681
 
1.8%
535924 2260
 
1.5%

num_of_top_instructor_courses
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.83459
Minimum2
Maximum1675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:33.223740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19
median60
Q3129
95-th percentile325
Maximum1675
Range1673
Interquartile range (IQR)120

Descriptive statistics

Standard deviation218.35251
Coefficient of variation (CV)1.9351557
Kurtosis33.3596
Mean112.83459
Median Absolute Deviation (MAD)51
Skewness5.1742424
Sum16836501
Variance47677.819
MonotonicityNot monotonic
2025-01-14T23:01:33.680989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
60 40172
26.9%
325 27713
18.6%
9 11707
 
7.8%
4 9256
 
6.2%
129 8173
 
5.5%
5 6648
 
4.5%
18 6522
 
4.4%
8 5137
 
3.4%
2 5113
 
3.4%
12 4903
 
3.3%
Other values (19) 23870
16.0%
ValueCountFrequency (%)
2 5113
3.4%
3 849
 
0.6%
4 9256
6.2%
5 6648
4.5%
6 1575
 
1.1%
7 2995
 
2.0%
8 5137
3.4%
9 11707
7.8%
10 50
 
< 0.1%
12 4903
3.3%
ValueCountFrequency (%)
1675 2060
 
1.4%
325 27713
18.6%
196 92
 
0.1%
129 8173
 
5.5%
87 177
 
0.1%
66 20
 
< 0.1%
60 40172
26.9%
58 2543
 
1.7%
53 1526
 
1.0%
48 12
 
< 0.1%

num_of_top_instructor_leaners
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3635020.6
Minimum3945
Maximum11153139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:33.849525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3945
5-th percentile167950
Q1517137
median1245385
Q34384601
95-th percentile11153139
Maximum11153139
Range11149194
Interquartile range (IQR)3867464

Descriptive statistics

Standard deviation3938650.6
Coefficient of variation (CV)1.0835291
Kurtosis-0.33807788
Mean3635020.6
Median Absolute Deviation (MAD)1124756
Skewness1.0471321
Sum5.4239596 × 1011
Variance1.5512968 × 1013
MonotonicityNot monotonic
2025-01-14T23:01:34.014452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4384601 40172
26.9%
11153139 27713
18.6%
954353 8173
 
5.5%
1071441 6276
 
4.2%
517137 4588
 
3.1%
596976 4526
 
3.0%
1245385 4499
 
3.0%
506004 4131
 
2.8%
426823 3934
 
2.6%
528545 3887
 
2.6%
Other values (73) 41315
27.7%
ValueCountFrequency (%)
3945 8
 
< 0.1%
6855 7
 
< 0.1%
7841 22
< 0.1%
8506 44
< 0.1%
8682 8
 
< 0.1%
8703 10
 
< 0.1%
18193 24
< 0.1%
19837 20
< 0.1%
21526 45
< 0.1%
29959 45
< 0.1%
ValueCountFrequency (%)
11153139 27713
18.6%
4384601 40172
26.9%
3081791 1526
 
1.0%
2767486 2060
 
1.4%
1245385 4499
 
3.0%
1071441 6276
 
4.2%
1065753 92
 
0.1%
1014757 2543
 
1.7%
954353 8173
 
5.5%
892947 1
 
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct105374
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.855965
Minimum-100
Maximum-8.7987732
Zeros0
Zeros (%)0.0%
Negative149214
Negative (%)100.0%
Memory size1.1 MiB
2025-01-14T23:01:34.168904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-83.082419
Q1-81.751176
median-63.863248
Q3-31.540263
95-th percentile-13.037956
Maximum-8.7987732
Range91.201227
Interquartile range (IQR)50.210913

Descriptive statistics

Standard deviation27.126242
Coefficient of variation (CV)-0.47710459
Kurtosis-1.2536669
Mean-56.855965
Median Absolute Deviation (MAD)18.096053
Skewness0.46187795
Sum-8483705.9
Variance735.83298
MonotonicityNot monotonic
2025-01-14T23:01:34.329583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-81.02716281 5367
 
3.6%
-100 3828
 
2.6%
-13.03795572 2105
 
1.4%
-11.50166211 1672
 
1.1%
-80.93541039 1549
 
1.0%
-80.89876638 1395
 
0.9%
-80.86473527 1323
 
0.9%
-12.66063664 941
 
0.6%
-82.00227236 740
 
0.5%
-12.67459405 728
 
0.5%
Other values (105364) 129566
86.8%
ValueCountFrequency (%)
-100 3828
2.6%
-85.13329371 5
 
< 0.1%
-85.0804667 1
 
< 0.1%
-85.05122223 1
 
< 0.1%
-85.04970454 5
 
< 0.1%
-84.99239666 2
 
< 0.1%
-84.94088167 11
 
< 0.1%
-84.93204365 5
 
< 0.1%
-84.90706193 1
 
< 0.1%
-84.842206 7
 
< 0.1%
ValueCountFrequency (%)
-8.798773191 1
< 0.1%
-8.839094015 1
< 0.1%
-9.126066095 1
< 0.1%
-9.415730643 1
< 0.1%
-9.534633016 1
< 0.1%
-9.581133571 1
< 0.1%
-9.650485166 1
< 0.1%
-9.706895053 1
< 0.1%
-9.77316162 1
< 0.1%
-9.778012839 1
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct71197
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1792504
Minimum0.031277315
Maximum4.4330168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:34.489545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.031277315
5-th percentile0.3790408
Q10.66030992
median0.69947137
Q31.4814601
95-th percentile2.8867949
Maximum4.4330168
Range4.4017395
Interquartile range (IQR)0.82115021

Descriptive statistics

Standard deviation0.92142172
Coefficient of variation (CV)0.78136222
Kurtosis1.8066983
Mean1.1792504
Median Absolute Deviation (MAD)0.17354258
Skewness1.6048744
Sum175960.67
Variance0.84901799
MonotonicityNot monotonic
2025-01-14T23:01:34.650784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6994713653 33614
 
22.5%
2.840657022 5702
 
3.8%
0.647271721 3828
 
2.6%
0.6994713653 3625
 
2.4%
2.886794893 2310
 
1.5%
1.493976116 2105
 
1.4%
1.079430014 1672
 
1.1%
0.6994713653 1660
 
1.1%
3.98605896 1647
 
1.1%
4.433016776 1486
 
1.0%
Other values (71187) 91565
61.4%
ValueCountFrequency (%)
0.03127731513 1
< 0.1%
0.03664649936 1
< 0.1%
0.0372592074 1
< 0.1%
0.03727451991 1
< 0.1%
0.03946053291 1
< 0.1%
0.04705684249 1
< 0.1%
0.04849854865 1
< 0.1%
0.05012946143 1
< 0.1%
0.05067275 1
< 0.1%
0.05178846861 1
< 0.1%
ValueCountFrequency (%)
4.433016776 1486
1.0%
4.432271889 1
 
< 0.1%
4.432031292 1
 
< 0.1%
4.432031292 1
 
< 0.1%
4.429161311 1
 
< 0.1%
4.42762739 1
 
< 0.1%
4.427098982 1
 
< 0.1%
4.426473879 3
 
< 0.1%
4.425900817 1
 
< 0.1%
4.424742466 3
 
< 0.1%

Topic_1
Real number (ℝ)

Distinct105126
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395973
Minimum5.3290677 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:34.802801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.3290677 × 10-20
5-th percentile1.5481282 × 10-19
Q15.856691 × 10-19
median1.5930932 × 10-16
Q30.02880365
95-th percentile0.91754365
Maximum1
Range1
Interquartile range (IQR)0.02880365

Descriptive statistics

Standard deviation0.28630888
Coefficient of variation (CV)2.0509629
Kurtosis2.5326214
Mean0.1395973
Median Absolute Deviation (MAD)1.5923608 × 10-16
Skewness1.9807456
Sum20829.872
Variance0.081972776
MonotonicityNot monotonic
2025-01-14T23:01:34.968899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5702
 
3.8%
0.2 3828
 
2.6%
0.0006863514583 2105
 
1.4%
0.6571794953 1672
 
1.1%
8.618077415 × 10-201549
 
1.0%
7.920751382 × 10-201395
 
0.9%
7.323779722 × 10-201323
 
0.9%
0.0009106662844 941
 
0.6%
1.005246004 × 10-18740
 
0.5%
0.000725620286 728
 
0.5%
Other values (105116) 129231
86.6%
ValueCountFrequency (%)
5.3290677 × 10-205
< 0.1%
5.395065478 × 10-201
 
< 0.1%
5.559252313 × 10-208
< 0.1%
5.655224667 × 10-204
< 0.1%
5.908822294 × 10-201
 
< 0.1%
6.438760185 × 10-201
 
< 0.1%
6.662520289 × 10-201
 
< 0.1%
6.677452297 × 10-201
 
< 0.1%
6.904938611 × 10-201
 
< 0.1%
7.042513217 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 5702
3.8%
0.999963129 1
 
< 0.1%
0.9999088076 2
 
< 0.1%
0.9998697588 1
 
< 0.1%
0.9997541338 2
 
< 0.1%
0.9997495615 1
 
< 0.1%
0.9996295034 1
 
< 0.1%
0.9995859096 11
 
< 0.1%
0.9995805335 7
 
< 0.1%
0.9995183408 1
 
< 0.1%

Topic_2
Real number (ℝ)

Distinct104862
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13520356
Minimum5.3950655 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:35.145263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.3950655 × 10-20
5-th percentile1.064542 × 10-19
Q15.3238236 × 10-19
median5.9500189 × 10-18
Q30.043237642
95-th percentile0.79299301
Maximum1
Range1
Interquartile range (IQR)0.043237642

Descriptive statistics

Standard deviation0.27222436
Coefficient of variation (CV)2.0134407
Kurtosis2.4291564
Mean0.13520356
Median Absolute Deviation (MAD)5.8708114 × 10-18
Skewness1.9574023
Sum20174.265
Variance0.0741061
MonotonicityNot monotonic
2025-01-14T23:01:35.321974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.064542017 × 10-195367
 
3.6%
0.2 3828
 
2.6%
1 2310
 
1.5%
0.7507309443 2105
 
1.4%
0.0133303283 1672
 
1.1%
7.920751382 × 10-201395
 
0.9%
7.323779722 × 10-201323
 
0.9%
0.007786906364 941
 
0.6%
1.005246004 × 10-18740
 
0.5%
0.006227934868 728
 
0.5%
Other values (104852) 128805
86.3%
ValueCountFrequency (%)
5.395065478 × 10-201
 
< 0.1%
6.048540813 × 10-2012
< 0.1%
6.371805887 × 10-208
< 0.1%
6.584048406 × 10-201
 
< 0.1%
6.677452297 × 10-201
 
< 0.1%
6.76038295 × 10-201
 
< 0.1%
6.942250403 × 10-201
 
< 0.1%
7.042513217 × 10-201
 
< 0.1%
7.063694004 × 10-201
 
< 0.1%
7.108169707 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 2310
1.5%
0.9999957173 1
 
< 0.1%
0.9999936612 1
 
< 0.1%
0.9998702707 4
 
< 0.1%
0.9998576996 1
 
< 0.1%
0.9998312138 1
 
< 0.1%
0.9997458554 1
 
< 0.1%
0.9996967957 1
 
< 0.1%
0.999673719 1
 
< 0.1%
0.9996712481 1
 
< 0.1%

Topic_3
Real number (ℝ)

High correlation 

Distinct71963
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6157978
Minimum5.3950655 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:35.499606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.3950655 × 10-20
5-th percentile1.064542 × 10-19
Q10.24010873
median0.7405396
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75989127

Descriptive statistics

Standard deviation0.38913041
Coefficient of variation (CV)0.63191263
Kurtosis-1.5791553
Mean0.6157978
Median Absolute Deviation (MAD)0.2594604
Skewness-0.3118109
Sum91885.653
Variance0.15142247
MonotonicityNot monotonic
2025-01-14T23:01:35.658666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43200
29.0%
1.064542017 × 10-195367
 
3.6%
0.2 3828
 
2.6%
0.2401087263 2105
 
1.4%
0.3160353822 1672
 
1.1%
8.618077415 × 10-201549
 
1.0%
7.920751382 × 10-201395
 
0.9%
7.323779722 × 10-201323
 
0.9%
0.1871661144 941
 
0.6%
0.1662144547 728
 
0.5%
Other values (71953) 87106
58.4%
ValueCountFrequency (%)
5.395065478 × 10-201
 
< 0.1%
5.655224667 × 10-204
 
< 0.1%
5.908822294 × 10-201
 
< 0.1%
6.048540813 × 10-2012
 
< 0.1%
6.371805887 × 10-208
 
< 0.1%
6.760106122 × 10-208
 
< 0.1%
6.942250403 × 10-201
 
< 0.1%
7.108169707 × 10-201
 
< 0.1%
7.323779722 × 10-201323
0.9%
7.382203295 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 43200
29.0%
1 437
 
0.3%
1 701
 
0.5%
1 261
 
0.2%
1 256
 
0.2%
1 5
 
< 0.1%
1 4
 
< 0.1%
1 8
 
< 0.1%
1 4
 
< 0.1%
1 3
 
< 0.1%

Topic_4
Real number (ℝ)

Distinct105184
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063106876
Minimum5.6552247 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:35.822341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.6552247 × 10-20
5-th percentile1.064542 × 10-19
Q14.1226256 × 10-19
median1.7937712 × 10-18
Q30.007812483
95-th percentile0.62149073
Maximum1
Range1
Interquartile range (IQR)0.007812483

Descriptive statistics

Standard deviation0.1963645
Coefficient of variation (CV)3.1116181
Kurtosis11.567587
Mean0.063106876
Median Absolute Deviation (MAD)1.687317 × 10-18
Skewness3.5292257
Sum9416.4295
Variance0.038559017
MonotonicityNot monotonic
2025-01-14T23:01:35.981485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.064542017 × 10-195367
 
3.6%
0.2 3828
 
2.6%
0.005376353675 2105
 
1.4%
0.007812483007 1672
 
1.1%
1 1647
 
1.1%
8.618077415 × 10-201549
 
1.0%
7.323779722 × 10-201323
 
0.9%
0.8008758429 941
 
0.6%
1.005246004 × 10-18740
 
0.5%
0.003921387615 728
 
0.5%
Other values (105174) 129314
86.7%
ValueCountFrequency (%)
5.655224667 × 10-204
 
< 0.1%
6.048540813 × 10-2012
 
< 0.1%
6.584048406 × 10-201
 
< 0.1%
6.760106122 × 10-208
 
< 0.1%
7.323779722 × 10-201323
0.9%
7.350288664 × 10-201
 
< 0.1%
7.641451814 × 10-2026
 
< 0.1%
7.680963948 × 10-201
 
< 0.1%
7.828774055 × 10-201
 
< 0.1%
7.892669853 × 10-2011
 
< 0.1%
ValueCountFrequency (%)
1 1647
1.1%
0.9999546443 1
 
< 0.1%
0.9999429451 1
 
< 0.1%
0.9998991993 2
 
< 0.1%
0.99980682 1
 
< 0.1%
0.9997005693 1
 
< 0.1%
0.9996559219 1
 
< 0.1%
0.9996555504 1
 
< 0.1%
0.9996550945 1
 
< 0.1%
0.9995929489 1
 
< 0.1%

Topic_5
Real number (ℝ)

Distinct105234
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046294455
Minimum5.9088223 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T23:01:36.148122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.9088223 × 10-20
5-th percentile1.064542 × 10-19
Q13.8432704 × 10-19
median1.4431437 × 10-18
Q30.0045730126
95-th percentile0.2
Maximum1
Range1
Interquartile range (IQR)0.0045730126

Descriptive statistics

Standard deviation0.17282332
Coefficient of variation (CV)3.7331321
Kurtosis18.609916
Mean0.046294455
Median Absolute Deviation (MAD)1.3366895 × 10-18
Skewness4.3918155
Sum6907.7808
Variance0.029867898
MonotonicityNot monotonic
2025-01-14T23:01:36.302708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.064542017 × 10-195367
 
3.6%
0.2 3828
 
2.6%
0.003097624325 2105
 
1.4%
0.005642311179 1672
 
1.1%
8.618077415 × 10-201549
 
1.0%
1 1486
 
1.0%
7.920751382 × 10-201395
 
0.9%
0.003260470057 941
 
0.6%
1.005246004 × 10-18740
 
0.5%
0.8229106025 728
 
0.5%
Other values (105224) 129403
86.7%
ValueCountFrequency (%)
5.908822294 × 10-201
 
< 0.1%
6.371805887 × 10-208
< 0.1%
6.438760185 × 10-201
 
< 0.1%
6.662520289 × 10-201
 
< 0.1%
6.760106122 × 10-208
< 0.1%
7.063694004 × 10-201
 
< 0.1%
7.429860443 × 10-201
 
< 0.1%
7.69340792 × 10-201
 
< 0.1%
7.693490205 × 10-201
 
< 0.1%
7.8032896 × 10-203
 
< 0.1%
ValueCountFrequency (%)
1 1486
1.0%
0.9999543232 1
 
< 0.1%
0.9999378798 2
 
< 0.1%
0.9997422203 1
 
< 0.1%
0.9996442061 1
 
< 0.1%
0.9996104282 1
 
< 0.1%
0.9995645355 1
 
< 0.1%
0.9995362649 3
 
< 0.1%
0.9994816321 1
 
< 0.1%
0.9994624143 1
 
< 0.1%

Interactions

2025-01-14T23:01:24.475576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:47.469768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2025-01-14T23:00:58.571596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2025-01-14T23:01:04.912422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2025-01-14T23:01:01.039069image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:04.102204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:07.170628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:10.212519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:12.970578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:15.234601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:17.347607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:19.765668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:21.870543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:23.954442image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:26.173388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:49.726719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:52.350063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:55.182078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:58.033419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:01.220349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:04.325339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:07.346505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:10.418517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:13.210322image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:15.363544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:17.477476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:19.904835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:22.000711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:24.087844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:26.319875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:49.892918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:52.518219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:55.378058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:58.223947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:01.429237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:04.517413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:07.529314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:10.607782image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:13.379640image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:15.507687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:17.615670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:20.048196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:22.139444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:24.220008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:26.459773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:50.062026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:52.724536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:55.561128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:00:58.398592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:01.627584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:04.705072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:07.706761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:10.779269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:13.537428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:15.639186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:17.750867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:20.186261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:22.286301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T23:01:24.346873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-01-14T23:01:36.448575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
RatingTopic_1Topic_2Topic_3Topic_4Topic_5avg_ratingbreadthdepthhelpfulnessnum_of_enrollednum_of_ratingsnum_of_reviewsnum_of_top_instructor_coursesnum_of_top_instructor_leanersposted_date
Rating1.0000.0630.0420.0640.0440.0230.1180.0700.0610.0460.1130.1140.1190.0720.0930.033
Topic_10.0631.0000.032-0.3440.1670.256-0.126-0.0930.3500.024-0.100-0.095-0.090-0.059-0.0900.029
Topic_20.0420.0321.000-0.2340.2640.3320.012-0.1450.4120.005-0.025-0.025-0.0240.0330.0110.023
Topic_30.064-0.344-0.2341.000-0.055-0.0200.087-0.599-0.5170.0370.0870.0810.0780.0600.0720.003
Topic_40.0440.1670.264-0.0551.0000.487-0.019-0.2470.3360.015-0.066-0.056-0.056-0.017-0.0420.015
Topic_50.0230.2560.332-0.0200.4871.000-0.062-0.3040.3350.016-0.079-0.075-0.069-0.009-0.0420.041
avg_rating0.118-0.1260.0120.087-0.019-0.0621.000-0.032-0.027-0.1150.4920.5720.5300.3860.484-0.004
breadth0.070-0.093-0.145-0.599-0.247-0.304-0.0321.0000.085-0.074-0.035-0.030-0.028-0.034-0.027-0.031
depth0.0610.3500.412-0.5170.3360.335-0.0270.0851.0000.023-0.011-0.0030.001-0.067-0.065-0.084
helpfulness0.0460.0240.0050.0370.0150.016-0.115-0.0740.0231.000-0.123-0.141-0.139-0.072-0.098-0.021
num_of_enrolled0.113-0.100-0.0250.087-0.066-0.0790.492-0.035-0.011-0.1231.0000.9380.9430.3530.635-0.126
num_of_ratings0.114-0.095-0.0250.081-0.056-0.0750.572-0.030-0.003-0.1410.9381.0000.9840.3840.605-0.215
num_of_reviews0.119-0.090-0.0240.078-0.056-0.0690.530-0.0280.001-0.1390.9430.9841.0000.3410.569-0.201
num_of_top_instructor_courses0.072-0.0590.0330.060-0.017-0.0090.386-0.034-0.067-0.0720.3530.3840.3411.0000.8510.323
num_of_top_instructor_leaners0.093-0.0900.0110.072-0.042-0.0420.484-0.027-0.065-0.0980.6350.6050.5690.8511.0000.187
posted_date0.0330.0290.0230.0030.0150.041-0.004-0.031-0.084-0.021-0.126-0.215-0.2010.3230.1871.000

Missing values

2025-01-14T23:01:26.997919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-14T23:01:27.453982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

course_nameposted_datecrawling_dateRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanersdepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5
0foundations-of-cybersecurity202307292024113054.82790554The course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field. It is very good for the beginners who are new to this field551098822332511153139-32.3540670.8370815.460677e-013.929813e-190.4444868.961206e-034.852317e-04
1foundations-of-cybersecurity202305212024113054.82790544Information was well organized, easy to learn, and study. with frequent note writing, and some breaks . You can learn a good brief summary of what's to come, and what to research more in the future.551098822332511153139-64.0238111.0711796.207771e-014.986782e-190.3792234.986782e-194.986782e-19
2foundations-of-cybersecurity202305152024113054.82790541For a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses. I'm eager to move on to the other courses now.551098822332511153139-81.7687860.6994715.871993e-195.871993e-191.0000005.871993e-195.871993e-19
3foundations-of-cybersecurity202401092024113054.82790532I think this is a great start for anyone who is starting from absolute zero. I think that since I've been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher!551098822332511153139-48.5127362.4059871.943115e-039.377472e-010.0603103.295356e-193.295356e-19
4foundations-of-cybersecurity202305192024113054.82790524Surprised by the quality of this course. repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable.551098822332511153139-63.5574871.0492612.939663e-196.030008e-010.3969992.939663e-192.939663e-19
5foundations-of-cybersecurity202310282024113054.82790521! Cybersecurity is a critical and multifaceted field that involves protecting computer systems, networks, and data from various digital threats. Here is a review of the foundations of cybersecurity:551098822332511153139-82.5611790.6994713.640648e-183.640648e-181.0000003.640648e-183.640648e-18
6foundations-of-cybersecurity202306172024113014.82790518In the certificate , why am i not getting my name printed ? Instead "Coursera Learner"?551098822332511153139-82.5281910.6994713.374357e-183.374357e-181.0000003.374357e-183.374357e-18
7foundations-of-cybersecurity202306102024113054.8279059Dear Instructors of the Foundations of Cybersecurity Course at Google,I wanted to take a moment to express my deepest gratitude for the incredible learning experience you provided throughout the course. Your expertise, dedication, and passion for cybersecurity have truly made a lasting impact on my journey in this field.Firstly, allow me to introduce myself. My name is [Your Name], and I embarked on this course with a strong desire to deepen my understanding of cybersecurity and acquire the necessary skills to contribute meaningfully to the industry. As a lifelong learner, I am always seeking opportunities to expand my knowledge and stay updated with the latest developments in technology. This course seemed like the perfect fit to enhance my cybersecurity expertise.I chose to take this course because I firmly believe that cybersecurity is a critical aspect of our increasingly digital world. With the growing threats and vulnerabilities that organizations and individuals face, I wanted to equip myself with the knowledge and skills to make a tangible difference in securing digital systems and protecting sensitive information. The Foundations of Cybersecurity course seemed like the ideal starting point to build a strong foundation in this field.I am pleased to share that the course has exceeded my expectations in every way. From the very beginning, the course structure, content, and delivery were impeccable. The way you organized the modules and topics ensured a smooth learning journey, allowing me to grasp the fundamental concepts before diving into more advanced areas.What I truly loved about the course was the emphasis on practicality. The hands-on labs and simulations were invaluable in reinforcing the theoretical knowledge and providing a real-world perspective. Being able to apply the concepts in a practical setting not only enhanced my technical skills but also instilled confidence in my ability to tackle real-world cybersecurity challenges.Furthermore, the breadth of topics covered in the course was remarkable. From threat analysis to network security, encryption, incident response, and compliance, every aspect was explored in depth, providing a comprehensive understanding of the cybersecurity landscape. I appreciated the balance between theory and practical applications, as it allowed me to develop a holistic understanding of the subject matter.Your dedication as instructors was evident throughout the course. The quality of the course materials, including the informative videos, interactive quizzes, and additional readings, showcased the meticulous effort put into curating the content. Your ability to simplify complex concepts and communicate them effectively is commendable.The course has had a significant impact on my professional growth. Not only have I gained a solid understanding of cybersecurity fundamentals, but I have also acquired practical skills that I can immediately apply in real-world scenarios. The course has expanded my career prospects and opened doors to exciting opportunities in the cybersecurity field.I am truly grateful for the knowledge and insights I gained from this course. Your guidance and expertise have played a crucial role in shaping my cybersecurity journey. The impact you have made on my professional development is immeasurable.Thank you once again for your dedication, passion, and commitment to providing an exceptional learning experience. I am proud to have been a student in the Foundations of Cybersecurity course at Google, and I look forward to continuing my cybersecurity journey with the skills and knowledge I have acquired.With utmost appreciation,Jalal Saleem551098822332511153139-15.8751800.4575225.238462e-033.088402e-020.9486428.217229e-037.018596e-03
8foundations-of-cybersecurity202305202024113034.8279059The questions on the quizzes were often meaningless and the multiple choice answers were unbelievably vague and ill-defined to the point where no answer was entirely correct.551098822332511153139-67.6008210.6209471.456140e-027.565311e-180.9854397.565311e-187.565311e-18
9foundations-of-cybersecurity202306142024113054.8279058The instructors for the course did an amazing job at presenting all of the information to us! The course is informative and definitely will expand your knowledge specifically over Cybersecurity.551098822332511153139-49.9130680.6349336.520329e-049.816652e-030.9895313.728866e-193.728866e-19
course_nameposted_datecrawling_dateRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanersdepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5
149204java-programming-arrays-lists-data202009162024113014.731680Not good515160710181071441-81.0271632.8406571.000000e+001.064542e-191.064542e-191.064542e-191.064542e-19
149205introduction-to-applied-cryptography202309052024113054.6660This course by Professor Keith Martin is a fast-paced introduction to the basics of cryptography and six important classes of applications.Even though the course is aimed at beginners, it contains a lot of material (e.g., on mobile telephony) that will be of interest to advanced students as well.What I appreciated most about the course was the broad perspective and the relaxed manner in which serious knowledge was taught.99693283454-14.7298200.3567591.836691e-022.151584e-029.150897e-013.176309e-021.326444e-02
149206introduction-to-applied-cryptography202305162024113054.6660Thank you so much during this 4 weeks, i gained more knowledges about cryptography in this course. This course really help me to learn in flexiblw time.99693283454-81.4843990.6994713.050698e-193.050698e-191.000000e+003.050698e-193.050698e-19
149207introduction-to-applied-cryptography202305162024113054.6660very useful and informative course.99693283454-81.5673670.6994713.692898e-193.692898e-191.000000e+003.692898e-193.692898e-19
149208introduction-to-applied-cryptography202309032024113054.6660Challenging and very enratainent.99693283454-82.5790360.6994713.793467e-183.793467e-181.000000e+003.793467e-183.793467e-18
149209introduction-to-applied-cryptography202311072024113054.6660very good education.99693283454-81.4960612.8406571.000000e+003.133724e-193.133724e-193.133724e-193.133724e-19
149210introduction-to-applied-cryptography202411122024113054.6660good to learn99693283454-63.3543542.0409428.773162e-011.560086e-191.226838e-011.560086e-191.560086e-19
149211introduction-to-applied-cryptography202311062024113054.6660It is Good99693283454-81.0271632.8406571.000000e+001.064542e-191.064542e-191.064542e-191.064542e-19
149212introduction-to-applied-cryptography202405172024113054.6660excellent99693283454-80.8987663.9860597.920751e-207.920751e-207.920751e-201.000000e+007.920751e-20
149213introduction-to-applied-cryptography202309292024113054.6660OK99693283454-69.0478830.5681563.040489e-025.737309e-179.695951e-015.737309e-175.737309e-17

Duplicate rows

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